CN119868763B - Feedback method and system based on microcatheter tail end force - Google Patents
Feedback method and system based on microcatheter tail end forceInfo
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Abstract
本发明涉及医疗技术领域，具体为一种基于微型导管末端力的反馈方法及系统，方法包括以下步骤：通过CT扫描获取患者血管的三维数据,并对三维数据进行预处理，从预处理后的三维数据中进行特征提取生成处理后的特征数据集；通过导管末端的力传感器采集接触力信号S，对采集的接触力信号进行小波变换，分解为不同分辨率下的近似系数和细节系数；对细节系数Dj进行卡尔曼滤波得到滤波后的估计信号，将滤波后的估计信号作为滤波后的细节系数；基于小波逆变换将近似系数Aj和滤波后的细节系数D′j重构为时域信号S′，将时域信号传递至导管控制系统；将CT影像、实时力信号和导管位置信息进行融合得到融合信号；能够提高了导管控制的精度。
This invention relates to the field of medical technology, specifically to a feedback method and system based on the force at the tip of a microcatheter. The method includes the following steps: acquiring three-dimensional data of a patient's blood vessels through CT scanning, preprocessing the three-dimensional data, and extracting features from the preprocessed three-dimensional data to generate a processed feature dataset; acquiring a contact force signal S through a force sensor at the catheter tip, performing wavelet transform on the acquired contact force signal to decompose it into approximation coefficients and detail coefficients at different resolutions; performing Kalman filtering on the detail coefficients Dj to obtain a filtered estimated signal, and using the filtered estimated signal as the filtered detail coefficients; reconstructing the approximation coefficients Aj and the filtered detail coefficients D′ j into a time-domain signal S′ based on inverse wavelet transform, and transmitting the time-domain signal to the catheter control system; fusing the CT images, real-time force signal, and catheter position information to obtain a fused signal; which improves the accuracy of catheter control.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a feedback method and a feedback system based on the end force of a microcatheter.
Background
Microcatheters are increasingly used in the field of minimally invasive medicine. Surgery generally requires extremely high precision and safety, and thus precise control of the microcatheter tip is particularly important, and conventional catheter control systems rely primarily on manual manipulation by the physician, but this approach is often limited by physician experience and intra-operative visual feedback, and it is difficult to achieve the desired control precision and safety.
In order to improve the accuracy and safety of catheter control, researchers have recently begun exploring the use of advanced signal processing techniques to optimize catheter control systems. However, the existing signal processing methods, such as fourier transform, although excellent in processing stationary signals, have a large limitation in processing non-stationary signals, particularly signals that frequently vary in the time dimension, such as human waveforms. Fourier transforms do not provide local information of the signal in the time domain and thus it is difficult to accurately capture the dynamic changes that occur when the catheter is moving within the body.
Furthermore, although some research attempts have been made to apply filtering techniques to catheter control systems to eliminate noise and interference, these methods tend to be too simple to effectively cope with complex noise environments and dynamic changes in the human system. Thus, in actual surgery, these methods still have difficulty providing stable, reliable catheter control.
For this purpose we propose a microcatheter tip force based feedback method and system to solve the above problems.
Disclosure of Invention
The invention aims to provide a feedback method and a feedback system based on microcatheter tail end force so as to solve the problems in the background art.
In order to achieve the above purpose, the invention provides a feedback method and a system based on the force of the tail end of a micro catheter, wherein the method comprises the following steps:
s1, acquiring three-dimensional data of a patient blood vessel through CT scanning, preprocessing the three-dimensional data, and extracting features from the preprocessed three-dimensional data to generate a processed feature data set;
S2, acquiring a contact force signal S through a force sensor at the tail end of the catheter, performing wavelet transformation on the acquired contact force signal, and decomposing the contact force signal into an approximation coefficient and a detail coefficient under different resolutions;
S3, carrying out Kalman filtering on the detail coefficient D j to obtain a filtered estimated signal, and taking the filtered estimated signal as a filtered detail coefficient;
S4, reconstructing the approximate coefficient A j and the filtered detail coefficient D j 'into a time domain signal S' based on wavelet inverse transformation, and transmitting the time domain signal to a catheter control system;
S5, fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal, and transmitting the fused signal to a catheter control system;
And S6, acquiring a time domain signal and a fusion signal, determining an adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting parameters of the catheter control system based on a feedback mechanism.
Preferably, the step of acquiring the contact force signal S through the force sensor at the tail end of the catheter and performing wavelet transformation on the acquired contact force signal to be decomposed into the approximate coefficient and the detail coefficient under different resolutions comprises the steps of installing the force sensor at the tail end or the near end of the catheter and acquiring the contact force signal S (t) through the force sensor, wherein t represents time. The method comprises the steps of (a) obtaining a signal S (t) containing force and direction information when a catheter is in contact with a vessel wall, converting an acquired analog signal into a digital signal through an analog-to-digital converter (ADC), carrying out primary filtering on the acquired digital signal, carrying out normalization processing on the signal to unify the amplitude range of the signal, selecting a proper wavelet basis function according to the signal characteristics, determining the number n of wavelet decomposition layers according to the signal frequency range and resolution requirements, carrying out discrete Wavelet Transformation (WT) on the preprocessed contact force signal S (t) to decompose the contact force signal S (t) into approximation coefficients and detail coefficients under different resolutions, wherein the corresponding formula is WT (S) = { Aj, dj|j=1, 2, 3..n }, wherein Aj is the approximation coefficient of a j th layer, the detail coefficient of the j th layer is obtained through decomposition of each layer, and continuing the decomposition of the approximation coefficients until the set decomposition layer n is reached.
Preferably, the step of performing Kalman filtering on the detail coefficient D j to obtain a filtered estimated signal, wherein the step of taking the filtered estimated signal as the filtered detail coefficient comprises the steps of extracting a detail coefficient of a certain layer from a wavelet transformation result, initializing a state vector, a state transition matrix, an observation matrix, a process noise covariance matrix and an observation noise covariance matrix of the Kalman filter according to priori knowledge and signal characteristics, predicting a future state of the signal according to the priori model, predicting an error covariance matrix of state estimation, calculating Kalman gain by utilizing the observation noise covariance matrix and the prediction error covariance matrix, combining the Kalman gain according to the detail coefficient of the current layer and the predicted state, updating the state estimation to obtain the filtered estimated signal, and taking the Kalman filtered state estimation as the filtered detail coefficient.
Preferably, the step of reconstructing the approximation coefficient A j and the filtered detail coefficient D j 'into the time domain signal S' based on the wavelet inverse transform includes extracting the approximation coefficient A j and the detail coefficient D j′ processed by Kalman filtering or other denoising methods from the wavelet transform result, wherein j 'represents different decomposition levels, determining a wavelet basis function for wavelet inverse transformation, obtaining the lowest-frequency approximation coefficient, reconstructing the lowest-frequency approximation coefficient into a rough version of the time domain signal by using the wavelet inverse transform, combining the lowest-frequency approximation coefficient with the currently reconstructed signal by using the wavelet inverse transform for each detail coefficient level to recover the high-frequency component of the signal layer by layer, and combining the reconstruction results of all levels to obtain the time domain signal S'.
Preferably, the step of fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal comprises the steps of obtaining the position information of the catheter in a three-dimensional space to obtain the catheter position information, setting initial weights for the CT image, the real-time force signal and the catheter position information at the same time point, and calculating the weighted sum of the CT image, the real-time force signal and the catheter position information according to the weights to obtain the fused signal for each time point or each data point, wherein the corresponding calculation formula is as followsWherein M i is the ith mode data, w i is the weight of the ith mode data, and k is the total number of modes.
Preferably, the method comprises the steps of obtaining a time domain signal and a fusion signal, determining an adjustment strategy for a catheter control system based on the time domain signal and the fusion signal, adjusting parameters of the catheter control system based on a feedback mechanism, wherein the steps of obtaining the time domain signal and the fusion signal, extracting features of the preprocessed time domain signal, determining an adjustment direction of the catheter control system based on the features of the time domain signal, analyzing the fusion signal, extracting information directly related to operation, formulating a preliminary catheter control strategy based on the fusion signal and an operation target, combining analysis results of the time domain signal and the fusion signal, determining a target adjustment strategy for the catheter control system, designing a closed loop feedback system, comparing output of the catheter control system with an expected target, generating an error signal according to the comparison result, and adjusting the parameters of the catheter control system according to the size and the direction of the error signal.
A microcatheter tip force-based feedback system for use in a microcatheter tip force-based feedback method as in any of the preceding claims, comprising:
the data acquisition module is used for acquiring three-dimensional data of a patient blood vessel through CT scanning, preprocessing the three-dimensional data, and extracting features from the preprocessed three-dimensional data to generate a processed feature data set;
The first processing module is used for acquiring a contact force signal S through a force sensor at the tail end of the catheter, carrying out wavelet transformation on the acquired contact force signal, and decomposing the acquired contact force signal into an approximation coefficient and a detail coefficient under different resolutions;
The second processing module is used for carrying out Kalman filtering on the detail coefficient D j to obtain a filtered estimated signal, and taking the filtered estimated signal as a filtered detail coefficient;
A third processing module for reconstructing the approximation coefficient a j and the filtered detail coefficient D j 'into a time domain signal S' based on the wavelet inverse transform, delivering the time domain signal to the catheter control system;
The fusion module is used for fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;
And the feedback adjustment module is used for acquiring the time domain signal and the fusion signal, determining an adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting parameters of the catheter control system based on a feedback mechanism.
Compared with the prior art, the invention has the beneficial effects that:
Three-dimensional data of a blood vessel of a patient are obtained through CT scanning, preprocessing and feature extraction are carried out, and a feature data set reflecting the shape and structure of the blood vessel can be generated. Meanwhile, a force sensor at the tail end of the catheter is used for collecting contact force signals, wavelet transformation and Kalman filtering processing are carried out, noise and interference can be effectively removed, and the accuracy and reliability of the signals are improved. The processed data provides a more accurate control basis for the catheter control system, thereby improving the accuracy of catheter control.
2. And fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal containing rich information. The fusion signal can reflect the actual situation in the operation process more comprehensively, and provides more visual operation guidance for doctors. Meanwhile, the position and the posture of the catheter can be adjusted in real time based on the adjustment strategy of the catheter control system determined by the time domain signals and the fusion signals, so that the operation risk caused by improper operation is avoided, and the operation safety is enhanced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention;
Fig. 2 is a block diagram of the system architecture of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1 to 2, the present invention provides a feedback method and a system based on microcatheter tip force, comprising the following steps:
s1, acquiring three-dimensional data of a patient blood vessel through CT scanning, preprocessing the three-dimensional data, and extracting features from the preprocessed three-dimensional data to generate a processed feature data set;
Specifically, three-dimensional data of a patient blood vessel are obtained through CT scanning, noise in a CT image is removed through Gaussian filtering, definition of a blood vessel boundary is improved through an image enhancement technology, a three-dimensional blood vessel model is generated through extracting a blood vessel region through a threshold segmentation algorithm, local features (such as blood vessel diameter and tortuosity) of the blood vessel are extracted through a convolution kernel, global features (such as blood vessel branch structures) are extracted through a statistical method, and a preprocessed feature dataset D is generated, wherein D=f p (x) is used for intra-operation navigation and signal analysis;
Wherein D contains critical anatomical information such as vessel position, organ boundaries, etc., and eliminates irrelevant information that might interfere with subsequent signal analysis. The preprocessing stage also includes data normalization operations to ensure consistency in spatial resolution and intensity distribution of the CT images. Finally, a data representation matching the subsequent catheter navigation and signal analysis is generated by a feature construction algorithm. In the process, a convolution kernel can be utilized to extract three-dimensional local features, global features are extracted through a statistical method, and finally an output feature data set D can be used for intraoperative real-time processing;
S2, acquiring a contact force signal S through a force sensor at the tail end of the catheter, performing wavelet transformation on the acquired contact force signal, and decomposing the contact force signal into an approximation coefficient and a detail coefficient under different resolutions;
The method comprises the steps of collecting a contact force signal S through a force sensor at the tail end of a catheter, carrying out wavelet transformation on the collected contact force signal, and decomposing the contact force signal into an approximation coefficient and a detail coefficient under different resolutions, wherein the force sensor is arranged at the tail end or the near end of the catheter, and collecting the contact force signal S (t) through the force sensor, wherein t represents time. The method comprises the steps of (a) carrying out a discrete Wavelet Transform (WT) on a preprocessed contact force signal S (t), decomposing the preprocessed contact force signal S (t) into approximation coefficients and detail coefficients under different resolutions, and obtaining a corresponding formula of WT (S) = { A j,Dj |j=1, 2, 3..n }, wherein A j is an approximation coefficient of a j-th layer, and D j is a detail coefficient of the j-th layer, each layer of decomposition divides the signal into the approximation coefficient and the detail coefficient, and the approximation coefficient is continuously decomposed until the set decomposition layer number n is reached;
The selection basis of selecting proper wavelet base functions according to signal characteristics is that the time-frequency localization characteristics of the first wavelet base functions are the same as those of the second wavelet base functions;
The method comprises the steps of acquiring a contact force signal S through a force sensor at the tail end of a catheter in operation, carrying out wavelet transformation on the acquired force signal, and decomposing the acquired force signal into an approximate coefficient (low-frequency part) and a detail coefficient (high-frequency part), wherein the WT (S) = { A j,Dj |j=1, 2, 3..n }, wherein A j is the approximate coefficient (low-frequency part) and represents the overall trend of the signal, D j is the detail coefficient (high-frequency part) and represents the local change characteristic of the signal, n is the decomposition layer number, the approximate coefficient A j is used for capturing the overall trend of the force, and the detail coefficient D j is used for analyzing the local change characteristic of the force;
By selecting a suitable wavelet basis function, a balance between the time domain and the frequency domain can be achieved. The aim of this stage is to lay a foundation for signal denoising and feature extraction while reducing computational complexity. In the application in surgery, the decomposition of different scales can more intuitively show the change of key components in signals, and is helpful for accurately positioning a catheter and analyzing a focus area;
The approximation coefficient A j is used for capturing the overall trend of the contact force signal and reflects the main change of the contact force of the catheter and the blood vessel wall. Can be used for analyzing the overall motion state (such as pushing and rotating) of the catheter.
The detail coefficient D j is used for analyzing the local change characteristic of the contact force signal and reflecting the tiny fluctuation when the catheter is contacted with the blood vessel wall. Can be used to detect abnormal force signals (e.g., bumps, rubs).
And denoising the detail coefficient, namely carrying out threshold denoising on the high-frequency detail coefficient D j to remove noise interference. The denoising method includes a hard threshold and a soft threshold.
And extracting key features, namely extracting approximate coefficient features, namely the mean value and the trend change of the force from the approximate coefficient A j and the denoised detail coefficient D j. The detail coefficient is characterized by fluctuation frequency and amplitude variation of force;
S3, carrying out Kalman filtering on the detail coefficient D j to obtain a filtered estimated signal, and taking the filtered estimated signal as a filtered detail coefficient;
The method comprises the steps of carrying out Kalman filtering on a detail coefficient D j to obtain a filtered estimation signal, taking the filtered estimation signal as a filtered detail coefficient, extracting a detail coefficient of a certain layer from a wavelet transformation result, initializing a state vector, a state transition matrix, an observation matrix, a process noise covariance matrix and an observation noise covariance matrix of the Kalman filter according to priori knowledge and signal characteristics, predicting an error covariance matrix of state estimation according to a future state of a priori model, calculating Kalman gain by utilizing the observation noise covariance matrix and the prediction error covariance matrix, combining the Kalman gain according to the detail coefficient of a current layer and the predicted state, updating state estimation to obtain a filtered estimation signal, and taking the Kalman filtered state estimation as the filtered detail coefficient;
Specifically, let D j be the detail coefficient of a certain layer, the filtered estimation signal be represented as D j′,Dj′＝KF(Dj), where KF represents a kalman filter function; according to the prior model, predicting the future state of the signal, adjusting the prediction result according to the actual observed value, and carrying out optimal estimation on the signal to obtain a filtered estimated signal, wherein the filtered detail coefficient D j' keeps the local characteristics of the signal and reduces noise interference;
The filtering process is divided into two steps, firstly, predicting the future state of the signal according to an priori model (namely a state transition matrix), and secondly, adjusting the prediction result according to the actual observed value to obtain a more accurate estimated value. In intraoperative applications, high frequency components are often associated with noise, so kalman filtering is mainly used for optimization of high frequency signals to reduce the effect of spurious signals on catheter control;
S4, reconstructing the approximate coefficient A j and the filtered detail coefficient D j 'into a time domain signal S' based on wavelet inverse transformation, and transmitting the time domain signal to a catheter control system;
The step of reconstructing the approximation coefficient A j and the filtered detail coefficient D j 'into a time domain signal S' based on wavelet inverse transformation comprises extracting the approximation coefficient A j and the detail coefficient D j′ processed by Kalman filtering or other denoising methods from the wavelet transformation result, wherein j 'represents different decomposition levels, and determining a wavelet basis function for wavelet inverse transformation; obtaining the approximation coefficient of the lowest frequency, reconstructing the approximation coefficient of the lowest frequency into a rough version of the time domain signal by using wavelet inverse transformation, combining each detail coefficient level with the current reconstructed signal by using wavelet inverse transformation to restore the high frequency component of the signal layer by layer, and combining the reconstruction results of all levels to obtain a time domain signal S';
Specifically, the approximation coefficient a j and the filtered detail coefficient D j 'are obtained, the approximation coefficient a j and the filtered detail coefficient D j are reconstructed into a time domain signal S' based on the inverse wavelet transform, and the corresponding signal reconstruction formula is S '=wt -1(Aj,Dj'), where the WT -1 represents the inverse wavelet transform, and typically, the reconstruction process starts with the detail coefficient of the highest frequency (if there are multiple levels), but in practice, from the multi-scale decomposition of the wavelet transform, the reconstruction starts with the approximation coefficient of the lowest frequency, and then the detail coefficient is added layer by layer. The application is described herein in terms of a reconstruction sequence for clarity of logic, using wavelet inverse transforms to reconstruct the approximation coefficients of the lowest frequency into a coarse version of the time domain signal. This step corresponds to reconstructing the low frequency component of the signal, and for each level of detail coefficients (from high frequency to low frequency, or according to a specific decomposition order) combining it with the currently reconstructed signal using wavelet inverse transform to recover the high frequency component of the signal layer by layer. This step is achieved by convolving and summing the detail coefficients with the corresponding wavelet functions so as to refine the reconstructed signal step by step, and combining the reconstructed results of all levels to obtain the final reconstructed signal S'. Ensuring that all frequency components of the signal are accurately recovered;
The formula corresponding to the filtered approximation coefficient Aj and the detail coefficient Dj ' are reconstructed into a time domain signal S ' by using wavelet inverse transformation is S ' =WT -1(Aj,Dj '), wherein WT -1 represents wavelet inverse transformation, the reconstructed signal S ' is transmitted to a catheter control system for adjusting catheter motion in real time, and the process ensures that the denoised signal has high fidelity in the time domain and is suitable for intraoperative real-time transmission and analysis. The key to signal reconstruction is to preserve the temporal characteristics of the original signal while minimizing the disruption of its structure by noise. The reconstructed signal S 'S' is transmitted to a catheter control system for guiding the catheter to move and adjust the position, and the catheter control system adjusts the movement and the position of the catheter in real time according to the received reconstructed time domain signal, so that the final purpose of signal processing is realized, namely guiding the catheter to move in operation;
S5, fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal, and transmitting the fused signal to a catheter control system;
The step of fusing CT image, real-time force signal and catheter position information to obtain fused signal includes obtaining catheter position information by obtaining catheter position information in three-dimensional space, setting initial weight for CT image, real-time force signal and catheter position information at the same time point, calculating weighted sum of CT image, real-time force signal and catheter position information according to weight to obtain fused signal for each time point or data point, and corresponding calculation formula is that Wherein M i is the ith mode data, wi is the weight of the ith mode data, and k is the total number of modes;
In particular, the weighted fusion process itself helps to reduce the interference of spurious signals, since the lower weighted modal data has less impact on the fusion result. In addition, false signals can be further eliminated through additional signal processing technologies (such as threshold setting, signal morphology analysis and the like), and CT images, real-time force signals and catheter position information are effectively fused to obtain a fused signal with high reliability and accuracy;
Specifically, the CT image, the real-time force signal and the catheter position information are fused; the system can synthesize the advantages of various information sources through a weighted fusion algorithm, eliminate the interference of the false signals and improve the reliability and the accuracy of the signals. In addition, the fusion algorithm can dynamically adjust the weight through a learning mechanism, so that the best performance can be ensured under different operation scenes;
S6, acquiring a time domain signal and a fusion signal, determining an adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting parameters of the catheter control system based on a feedback mechanism;
The method comprises the steps of obtaining a time domain signal and a fusion signal, determining an adjustment strategy for a catheter control system based on the time domain signal and the fusion signal, adjusting parameters of the catheter control system based on a feedback mechanism, wherein the steps of obtaining the time domain signal and the fusion signal, extracting characteristics of the preprocessed time domain signal, determining an adjustment direction of the catheter control system based on the characteristics of the time domain signal, analyzing the fusion signal, extracting information directly related to operation, formulating a preliminary catheter control strategy based on the fusion signal and an operation target, combining analysis results of the time domain signal and the fusion signal, determining a target adjustment strategy of the catheter control system, designing a closed loop feedback system, comparing output of the catheter control system with an expected target, generating an error signal according to the comparison result, and adjusting the parameters of the catheter control system according to the size and the direction of the error signal;
Specifically, physical quantities such as force, displacement, speed and the like in the catheter operation process are acquired in real time through a sensor, and the physical quantities change along with time to form a time domain signal. And carrying out preprocessing operations such as filtering, denoising and the like on the acquired time domain signals so as to improve the accuracy and reliability of the signals. And carrying out weighted fusion on the multi-mode data such as the CT image, the real-time force signal, the catheter position information and the like to obtain a fusion signal. The fusion signal should comprehensively reflect key information during catheter procedures such as anatomy, tissue stiffness, catheter position, etc. Analyzing the preprocessed time domain signals to identify anomalies or critical events during catheter operation, such as sudden increases in force, sudden changes in displacement, and the like. According to the characteristics of the time domain signals, the adjustment direction of the catheter control system is preliminarily determined, such as slowing down speed, increasing strength and the like. The fusion signal is analyzed to extract information directly related to the surgical procedure, such as the relative position of the catheter and surrounding tissue, tissue hardness distribution, etc. In combination with the fusion signal and the surgical target, detailed catheter control strategies are formulated, such as adjusting the catheter path, avoiding sensitive areas, etc. And combining analysis results of the time domain signals and the fusion signals, comprehensively considering factors such as operation safety, efficiency and accuracy, and determining a final catheter control system adjustment strategy. A closed loop feedback system is designed to compare the output of the catheter control system (e.g., catheter position, speed, etc.) to a desired target. And generating an error signal according to the comparison result, and taking the error signal as a basis for adjusting parameters of the catheter control system. Parameters of the catheter control system are adjusted according to the magnitude and direction of the error signal and a preset control algorithm (such as PID control, adaptive control and the like). The parameter adjustment ensures that the catheter can stably and accurately track the expected path and avoid unnecessary damage to surrounding tissues, can determine an adjustment strategy for a catheter control system based on time domain signals and fusion signals, and adjusts system parameters in real time through a feedback mechanism so as to realize accurate control of catheter operation and safety and high efficiency of the operation process;
The method comprises the steps of obtaining real-time feedback signals through a force sensor and a position sensor, combining preset path planning and real-time feedback signals, adjusting the rotation, the propelling speed and the bending angle of a catheter, setting a safety force threshold value, detecting and warning abnormal force values in real time, realizing flexible contact of the catheter through main power control, resistance compensation and a steady force retaining mechanism, and carrying out real-time fine adjustment on a catheter system according to a reconstruction signal and a fusion result so as to adapt to the environment of dynamic change in operation. The parameters are adjusted through a feedback mechanism, so that the movement mode of the tail end of the catheter is optimized. Specifically, the feedback signals are acquired through the sensor, and the control algorithm adjusts the rotation, the advancing speed and the bending angle of the catheter by combining with the preset path planning and the signal characteristics calculated in real time, so that the accurate positioning and the safety of the catheter are ensured.
A microcatheter tip force-based feedback system for use in a microcatheter tip force-based feedback method as in any of the preceding claims, comprising:
the data acquisition module is used for acquiring three-dimensional data of a patient blood vessel through CT scanning, preprocessing the three-dimensional data, and extracting features from the preprocessed three-dimensional data to generate a processed feature data set;
The first processing module is used for acquiring a contact force signal S through a force sensor at the tail end of the catheter, carrying out wavelet transformation on the acquired contact force signal, and decomposing the acquired contact force signal into an approximation coefficient and a detail coefficient under different resolutions;
The second processing module is used for carrying out Kalman filtering on the detail coefficient D j to obtain a filtered estimated signal, and taking the filtered estimated signal as a filtered detail coefficient;
A third processing module for reconstructing the approximation coefficient a j and the filtered detail coefficient D j 'into a time domain signal S' based on the wavelet inverse transform, delivering the time domain signal to the catheter control system;
The fusion module is used for fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;
And the feedback adjustment module is used for acquiring the time domain signal and the fusion signal, determining an adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting parameters of the catheter control system based on a feedback mechanism.
CT data of a patient blood vessel are collected, three-dimensional reconstruction is completed, an improved force feedback system is utilized, signals are transmitted to a robot main controller after analog-to-digital conversion and filtering processing, the purpose of eliminating false signals is achieved through multi-mode information fusion, meanwhile, a safety force threshold is set, and the motion mode of the tail end of the miniature catheter is adjusted according to force feedback information.
The method comprises the steps of collecting CT data of a patient blood vessel before an operation by a doctor and completing three-dimensional reconstruction, collecting contact force and direction data by a force feedback system through a force sensor at the tail end or the near end of a catheter in the operation, transmitting signals to a robot main controller after analog-digital conversion and filtering processing to ensure the instantaneity and accuracy of force information, eliminating errors and correcting false signals by fusion of the force data with image and position information, setting a safety force threshold value, and carrying out real-time detection and warning on abnormal force values to ensure the safety and accuracy of operation. In the process of eliminating errors, a wavelet transformation and Kalman filtering method is adopted, the system actively adjusts the motion of the catheter according to feedback force, realizes flexible contact through active force control, resistance compensation and steady-state force maintaining mechanism, and simultaneously transmits real-time force information to doctors through touch or visual feedback.
Compared with the traditional Fourier transform processing of digital signals, by the method, doctors can utilize wavelet transform to process and transmit the change of human waveform in the time dimension, and meanwhile, the Kalman filtering method can help doctors consider the influence of the change of human system on blood vessels. The influence can more scientifically correct errors caused by information detection in the dynamic change process of the human body.
In addition, the invention provides a novel digital signal processing method based on wavelet transformation and Kalman filtering algorithm. The use of wavelet transforms helps to capture local characteristics of the signal, especially in the higher frequency portions of the change, and the use of Kalman filtering algorithms can optimize signal estimation by taking into account characteristics of process noise and measurement noise, providing a physician in actual surgery with scientific navigation based on physiological activity in the patient.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
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